This notebook contains scripts that generate brain visualizations of the localized sensitivity improvements.
Loading required packages
import os
import numpy as np
import pandas as pd
import nibabel as nib
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from tqdm.notebook import tqdm
from cerebro import cerebro_brain_utils as cbu
from cerebro import cerebro_brain_viewer as cbv
Basic functions
def ensure_dir(file_name):
os.makedirs(os.path.dirname(file_name), exist_ok=True)
return file_name
def write_np(np_obj, file_path):
with open(file_path, 'wb') as outfile:
np.save(outfile, np_obj)
def load_np(file_path):
with open(file_path, 'rb') as infile:
return np.load(infile)
def plot_cifti_with_cerebro(axes, dscalar_file=None, dscalar_data=None, colormap=plt.cm.Spectral, **kwargs):
my_brain_viewer = cbv.Cerebro_brain_viewer(offscreen=True, background_color=(1,1,1,0),)
try:
surface = 'inflated'
surface_model = my_brain_viewer.load_template_GIFTI_cortical_surface_models(surface)
cifti_expansion_scale=40
cifti_left_right_seperation=20
volumetric_structure_offset=(0, 10, -80)
cifti_space = my_brain_viewer.visualize_cifti_space(
volumetric_structures='all',
cifti_expansion_scale=cifti_expansion_scale,
cifti_left_right_seperation=cifti_left_right_seperation,
volumetric_structure_offset=volumetric_structure_offset,
volume_rendering='surface',
)
dscalar_layer = my_brain_viewer.add_cifti_dscalar_layer(
dscalar_data=dscalar_data,
dscalar_file=dscalar_file,
colormap=colormap,
opacity=0.8,
**kwargs
)
ax = axes[0]
ax.axis('off')
view = ((-250, 200, 0), None, None, None)
camconf = my_brain_viewer.view_to_camera_config(view)
camconf = my_brain_viewer.zoom_camera_to_content(camconf)
camconf['camera_pos'] = tuple([x * 0.7 for x in camconf['camera_pos']])
my_brain_viewer.viewer.change_view(**camconf)
my_brain_viewer.offscreen_draw_to_matplotlib_axes(ax)
ax = axes[1]
ax.axis('off')
camconf = my_brain_viewer.view_to_camera_config("R")
camconf = my_brain_viewer.zoom_camera_to_content(camconf)
camconf['camera_pos'] = tuple([x * 0.7 for x in camconf['camera_pos']])
my_brain_viewer.viewer.change_view(**camconf)
my_brain_viewer.offscreen_draw_to_matplotlib_axes(ax)
plt.show()
finally:
my_brain_viewer.viewer.window.destroy()
return my_brain_viewer
Plot settings (latex is used for better plotting)
sns.set()
sns.set_style("darkgrid")
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
plt.rc('text', usetex=True)
plt.rc('text.latex', preamble=r'\usepackage{mathtools} \usepackage{sfmath}')
plt.rc('xtick', labelsize=20)
plt.rc('ytick', labelsize=20)
plt.rc('axes', labelsize=24)
plt.rc('figure', dpi=500)
The ground truth stored in notebook 2 is loaded here.
# list of all tasks and the cope number related to each selected contrast
tasks = {
'EMOTION': '3', # faces - shapes
'GAMBLING': '6', # reward - punish
'RELATIONAL': '4', # rel - match
'SOCIAL': '6', # tom - random
'WM': '20', # face - avg
}
# Compute mean and std, followed by a parametric z-score (one sample t-test)
ground_truth_effect = {}
# Base directory where files are stored at
base_dir='/data/netapp01/work/sina/structural_clustering/PALM_revision_1'
for task in tqdm(tasks, desc="Tasks loop", leave=True):
ground_truth_effect[task] = load_np(
'{}/ground_truth/cohen_d_{}_cope{}.dscalar.npy'.format(base_dir, task, tasks[task]),
)
Tasks loop: 0%| | 0/5 [00:00<?, ?it/s]
PALM results stored in notebook 1 is loaded here.
%%time
# Number of random repetitions
repetitions = 500
# Different sample sizes tested
sample_sizes = [10, 20, 40, 80, 160, 320]
# Different cluster defining thresholds
cdts = [3.3, 2.8, 2.6, 2.0, 1.6]
# Number of brainordinates in a cifti file
Nv = 91282
# Base directory where files are stored at
base_dir='/data/netapp01/work/sina/structural_clustering/PALM_revision_1'
# Store loaded results in nested python dictionaries
loaded_maps = {}
loaded_maps['uncorrected_tstat'] = {}
loaded_maps['spatial_cluster_corrected_tstat'] = {}
loaded_maps['topological_cluster_corrected_tstat'] = {}
# Only use the z=3.3, p=0.001 for the main analyses reported here
cdt = 3.3
sample_size = 40
for task in tqdm(tasks, desc="Tasks loop", leave=True):
loaded_maps['uncorrected_tstat'][task] = {}
loaded_maps['spatial_cluster_corrected_tstat'][task] = {}
loaded_maps['topological_cluster_corrected_tstat'][task] = {}
loaded_maps['uncorrected_tstat'][task][f'N={sample_size}'] = load_np(
f'{base_dir}/summary/uncorrected_tstat_{task}_{sample_size}_samples_{cdt}_CDT.npy',
)
loaded_maps['spatial_cluster_corrected_tstat'][task][f'N={sample_size}'] = load_np(
ensure_dir(f'{base_dir}/summary/spatial_cluster_corrected_tstat_{task}_{sample_size}_samples_{cdt}_CDT.npy'),
)
loaded_maps['topological_cluster_corrected_tstat'][task][f'N={sample_size}'] = load_np(
ensure_dir(f'{base_dir}/summary/topological_cluster_corrected_tstat_{task}_{sample_size}_samples_{cdt}_CDT.npy'),
)
Tasks loop: 0%| | 0/5 [00:00<?, ?it/s]
CPU times: user 35.6 ms, sys: 2.74 s, total: 2.77 s Wall time: 2.78 s
Cerebro brain viewer was used for brain visualizations.
mycm = matplotlib.colors.LinearSegmentedColormap.from_list(
'my_gradient',
(
(0.0, (0.1, 0.1, 1.,)),
(0.25, (0.1, 1., 1.,)),
(0.5, (1., 1., 1.,)),
(0.75, (1., 1., 0.1,)),
(1.0, (1., 0.1, 0.1,)),
)
)
fig, ax = plt.subplots(1, 1, figsize=(5, 1),)
ax.imshow(np.linspace(0, 1, 256)[np.newaxis, :].repeat(20,0), cmap=mycm)
ax.set_axis_off()
%%time
sample_size = 40
logp_threshold = -np.log10(0.05)
for ci, task in enumerate(tasks):
t_stats = loaded_maps['uncorrected_tstat'][task][f'N={sample_size}']
t_stats = t_stats[~np.isnan(t_stats).any(axis=1)]
topological_cluster_logps = loaded_maps['topological_cluster_corrected_tstat'][task][f'N={sample_size}']
topological_cluster_logps = topological_cluster_logps[~np.isnan(topological_cluster_logps).any(axis=1)]
topological_positive_effects = np.multiply(np.mean((topological_cluster_logps>logp_threshold) & (t_stats>0), 0), (ground_truth_effect[task]>0))
topological_negative_effects = np.multiply(np.mean((topological_cluster_logps>logp_threshold) & (t_stats<0), 0), (ground_truth_effect[task]<0))
spatial_cluster_logps = loaded_maps['spatial_cluster_corrected_tstat'][task][f'N={sample_size}']
spatial_cluster_logps = spatial_cluster_logps[~np.isnan(spatial_cluster_logps).any(axis=1)]
spatial_positive_effects = np.multiply(np.mean((spatial_cluster_logps>logp_threshold) & (t_stats>0), 0), (ground_truth_effect[task]>0))
spatial_negative_effects = np.multiply(np.mean((spatial_cluster_logps>logp_threshold) & (t_stats<0), 0), (ground_truth_effect[task]<0))
topological_sensitivity = (topological_positive_effects + topological_negative_effects)
spatial_sensitivity = (spatial_positive_effects + spatial_negative_effects)
localized_sensitivity_improvement = topological_sensitivity - spatial_sensitivity
print(task)
fig, axes = plt.subplots(1, 2, figsize=(10, 8),)
plt.subplots_adjust(wspace=0, hspace=0)
my_brain_viewer = plot_cifti_with_cerebro(
dscalar_data=localized_sensitivity_improvement,
colormap=mycm, axes=axes, clims=(-0.1, 0.1),
)
EMOTION
GAMBLING
RELATIONAL
SOCIAL
